Profiling user belief in BI exploration for measuring subjective - - PowerPoint PPT Presentation

Profiling user belief in BI exploration for measuring subjective interestingness

Alexandre Chanson, Ben Crulis, Krista Drushku, Nicolas Labroche, Patrick Marcel DOLAP 2019 - 26 March 2019

University of Tours

What is Alice best next move?

In fact, it depends!

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A very subjective question?

We would need to “brain dump” analysts

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What is subjective interestingness?

• Objective interestingness
• user agnostic, based only on data
• generality, reliability, peculiarity, diversity and conciseness,
• directly measurable evaluation metrics: support confidence, lift or chi-squared measures

in the case of association rules

• summaries: compact descriptions of raw data at different concept levels (Geng &

Hamilton)

• Subjective interestingness
• characterize the patterns’ surprise and novelty when compared to previous user

knowledge or expected data distribution

• user adaptive exploration
• subjective interestingness for explorative data mining

3

De Bie’s framework

• a pattern p ≈ restriction of data

space

• a belief(p) ≈ prior knowledge as a

probability distribution over the pattern space

• surprise(p) = −log(belief(p))

Interestingness(p) = surprise(p) |p|

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How to translate subjective interestingness to BI?

Two main problems:

• Define the ”pattern”
• Cell?
• Query?
• Query parts?
• Learn the belief function
• how to take into account the specificities of BI?
• how can we decide that two pieces of information are related in BI?
• do we consider the usage (the query logs)?
• do we consider the structure (the DB schema)?

5

Our proposal

Belief expressed over query parts

Classically, a query part is either:

• A group by set attribute
• A measure
• A selection predicate

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Query parts as patterns

Figure 1: Query as a restriction of the data space

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Our recipe so far

Figure 2: Caption

what ingredients we want to use ? knowing that the question is then: what is the probability that someone

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Random walk for learning the distribution

• consider a graph where vertices are query parts and edges are relations (precedence,

co-occurrence) between them

• the user does a random walk over this graph
• the long term distribution of the user gives a measure of importance of the query

parts

• it can be computed with a Page Rank
• or better, by a Topic-Specific Page Rank: a Page Rank where the user’s query parts are

more important than the others

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Baking the pie

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Experiments

Our ”Users”

• Artificial data generated with

CubeLoad [1]

• mimic prototypical explorations
• More ”consistent” than real users
• Less noisy
• Only 4 profiles

Figure 3: CubeLoad Templates

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Protocol of the qualitative experiment

• determine if there is a belief profile that is representative of each CubeLoad template

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Different user different beliefs

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Protocol of the quantitative experiment

Introducing a user agnostic recommender in the loop Robustness to logs exploring different regions (of the cube)

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Observing a cognitive bubble

Average Hellinger distance values on 10 runs when log files are identical

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Conclusions

• First attempt to model belief in BI
• Capture potential relations between user knowledge as a graph
• ⇒ use well-known Page-Rank for estimating probabilities
• Experiments
• Different simulated user templates == different beliefs distributions
• Possible detection of the cognitive bubble phenomena

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On-going and Future work

• What about belief distribution over cell contents?
• theoretically appealing but computationally painful...
• (but we’re on it)
• What about belief evolution along the exploration?
• Subjective interestingness is a trade-off between surprise and complexity of

description

• how to measure complexity of description in BI?
• How to validate a user “brain dump”?
• Perform a user study based on an improved query recommender system with

interestingness

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Long term vision

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Questions ?

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References i

• S. Rizzi and E. Gallinucci.

Cubeload: A parametric generator of realistic OLAP workloads. In Advanced Information Systems Engineering - 26th International Conference, CAiSE 2014, Thessaloniki, Greece, June 16-20, 2014. Proceedings, pages 610–624, 2014.